Medical　image　segmentation　is　a　crucial　and　intricate　process　in　medical　image　processing　and　analysis.　With　the　advancements　in　artificial　intelligence,　deep　learning　techniques　have　been　widely　used　in　recent　years　for　medical　image　segmentation.　One　such　technique　is　the　U-Net　framework　based　on　the　U-shaped　convolutional　neural　networks　(CNN)　and　its　variants.　However,　these　methods　have　limitations　in　simultaneously　capturing　both　the　global　and　the　remote　semantic　information　due　to　the　restricted　receptive　domain　caused　by　the　convolution　operation＇s　intrinsic　features.　Transformers　are　attention-based　models　with　excellent　global　modeling　capabilities,　but　their　ability　to　acquire　local　information　is　limited.　To　address　this,　we　propose　a　network　that　combines　the　strengths　of　both　CNN　and　Transformer,　called　CoTrFuse.　The　proposed　CoTrFuse　network　uses　EfficientNet　and　Swin　Transformer　as　dual　encoders.　The　Swin　Transformer　and　CNN　Fusion　module　are　combined　to　fuse　the　features　of　both　branches　before　the　skip　connection　structure.　We　evaluated　the　proposed　network　on　two　datasets:　the　ISIC-2017　challenge　dataset　and　the　COVID-QU-Ex　dataset.　Our　experimental　results　demonstrate　that　the　proposed　CoTrFuse　outperforms　several　state-of-the-art　segmentation　methods,　indicating　its　superiority　in　medical　image　segmentation.　The　codes　are　available　athttps://github.com/BinYCn/CoTrFuse.　Creative　Commons　Attribution　license.